I stopped sending JSON keys to my LLM 100 times. Here's what it saved (and where it didn't)

Ahmet Zencirli — Mon, 02 Nov 2026 11:21:25 +0000

Look at the next tool result your agent gets back from an API. Something like this:

[{"id":"A-1","status":"open","assignee":"ann","labels":["bug"]},
 {"id":"A-2","status":"closed","assignee":"bob","labels":[]},
 ...]

Now imagine 100 rows. The words id, status, assignee and labels are sent 100 times each, with quotes around every one. The model doesn't need them 100 times. It needs them once.

That's the whole idea behind LWON (Lightweight Object Notation): JSON with the keys written once. A header names the fields; the body holds only the values, by position.

What it looks like

{[orders[id customer[name city] [items[sku qty price]]]] page}
[[
[A-1 [Ann Istanbul] [[SKU1 2 9.99] [SKU2 1 19.5]]]
[A-2 [Bob Izmir] []]
] 1]

That is exactly this JSON:

{"orders":[{"id":"A-1","customer":{"name":"Ann","city":"Istanbul"},"items":[{"sku":"SKU1","qty":2,"price":9.99},{"sku":"SKU2","qty":1,"price":19.5}]},{"id":"A-2","customer":{"name":"Bob","city":"Izmir"},"items":[]}],"page":1}

Line 1 is the shape. Every row after it is just values. Nested objects and arrays of objects get their own fields in the header, so it stays compact even when the data isn't a flat table.

The hard requirement I set for myself: any JSON value must convert to LWON and back without loss. Escapes, null versus a missing key, objects with different fields, weird keys, primitive roots. The test suite runs a language-neutral conformance set plus 3000 fuzzed round trips.

"Isn't this just TOON / CSV?"

Fair question. TOON has the same goal and is very good on flat tables. CSV is fine until your data has nesting.

The difference shows up on the data agents actually see: API responses with nested objects, trees, and optional fields. Here are Claude token counts (via the count_tokens API) on six datasets:

Dataset	JSON (minified)	TOON	LWON
Flat table, 100 rows	4295	2510	2503
Nested orders	8259	7435	4808
GitHub-style issues	4378	4414	2760
Recursive menu	562	578	362
Logs with optional fields	4393	4472	3452
Search tool result	2930	2200	2298

On flat tables LWON and TOON are basically tied, and TOON wins the search result. On nested and recursive data, LWON is 21–42% smaller than minified JSON, while table formats fall back to layouts close to JSON's size.

Fewer tokens is worthless if the model gets it wrong

This was the part I cared about most. A format that saves 40% and makes the model misread a field is a net loss.

So I wrote an eval: 44 questions about the benchmark data (lookups, counts, nested fields, trees, absent versus null), asked in JSON, TOON and LWON, to Claude Haiku, Sonnet and Opus.

Every model got every question right in all three formats.

Then I made it harder: 150 sparse log events where most fields are missing in most rows. Here LWON missed 11 of 176 answers and JSON missed 9. All misses in both formats were counts over many rows off by one to three, or a sum.

I want to be upfront about that: on very sparse data, LWON is slightly behind JSON on accuracy, not ahead.

The thing I got wrong in 0.x

Input tokens are not the whole bill. Models think about the data, and output tokens cost more than input.

In LWON 0.x, every key was written by position, so sparse rows looked like [x ~ ~ ~ ~ ~ 3 ~ ~ ~]. The models had to count positions through all those ~ placeholders, reasoned much longer, and on the sparse data a question cost up to 3× as much as with JSON. Smaller input, bigger bill.

The fix in 1.0: rare keys are written by name in a trailing * slot. Common fields stay positional, rare ones stay readable.

Counting everything (input, thinking, output), a question now costs:

21–25% less than JSON on regular data sent once (2–15% less when cached)
9–11% less on sparse data sent once, but 6–9% more when cached

So: measure on your data. That's why the repo ships the eval scripts, not just the numbers.

Try it on your own JSON

The quickest way to see if LWON helps you is the playground. Paste any JSON, or load one of the benchmark datasets, and it converts both ways and shows the token count next to minified JSON.

Everything runs in your browser; your data isn't sent anywhere. Tokens there are counted with the o200k tokenizer, so the exact numbers differ a little from Claude's, but the ratio is close.

Using it

npm install @lwon/core

import { encode, decode, PRIMER } from '@lwon/core';

const text = encode(data);   // any JSON value → LWON
const back = decode(text);   // LWON → the same JSON value

// Models don't know LWON yet, so send a short primer (~330 tokens) once.
const prompt = `${PRIMER}\n\n${text}`;

One rule: convert before the data reaches the model, in your tool or API layer. Asking the model to convert saves nothing.

There's also a CLI if you want to check your own data first:

npx @lwon/core stats data.json   # JSON vs LWON size

Zero-code: an MCP proxy

Most of the JSON my agents see comes from MCP tool results, so I wrote @lwon/mcp-proxy. Put it in front of any stdio MCP server and large JSON tool results reach the model as LWON. The server doesn't change.

claude mcp add github -- npx -y @lwon/mcp-proxy -- npx -y @modelcontextprotocol/server-github

It only rewrites a result when it's JSON, at least 2000 characters, and at least 10% shorter as LWON. Errors, plain text, images and structuredContent pass through untouched. The primer goes into the server's instructions once per session, not once per result.

Status and what I'd love feedback on

LWON is 1.0 beta: the spec, a dependency-free TypeScript library, a conformance suite, and the MCP proxy, under Apache-2.0. Python and Go ports are next.

I'd really like to hear:

Your data. Paste a real tool result into the playground and tell me the number, especially if LWON loses.
Other models. My evals are Claude-only so far. If you test GPT or Gemini, I'll add the results.
The spec. If you find a JSON value that doesn't round-trip, that's a bug I want.
Playground: https://ahmetzencirli.github.io/lwon-studio/playground
Website and spec: https://ahmetzencirli.github.io/lwon-studio/
GitHub: https://github.com/ahmetzencirli/lwon

Thanks for reading!

DEV Community: Ahmet Zencirli